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Spanish researchers claim to have found a way to accurately predict how quickly and widely new pieces of information, or memes as they are called, will spread. The ability to forecast this viral behaviour would be of great interest to sociologists and marketeers, among others.

The secret, they say, is to recognise the fact that people vary in how "infectious" they are when it comes to sharing content online. While some people pass on things they receive right away, others do so after some delay, or not at all.

Medical models

The viral spread of information online has conventionally been modelled using epidemiological tools developed to analyse the spread of biological viruses. One of the concepts borrowed is that of an infection's R, or basic reproduction number, which describes how many other people someone with the virus can be expected to infect.

Moro, working with José Luis Iribarren at IBM in Madrid, used IBM's company e-mail newsletter to show the importance of variations between people's infectiousness in propagating memes online.

E-mail trail

They started a reward scheme offering prize draw tickets for recommending the newsletter by providing e-mail addresses of other people and tracked how widely and quickly the recommendations spread. After two months it had reached 31,000 people.

But while people took 1.5 days to respond to a recommendation e-mail on average, there was a huge variation at the individual level: some users responded within minutes, other in months, says Moro.

And only by combining some expectation of that variation with the R number is it possible to build a model able to predict the meme's spread. The team use a small chunk of the initial data on the content's spread to predict how many people it will reach in total, and how fast. "Our model can give predictions within 1 per cent error once secondary reproductive number and human activity are estimated," Moro says.

The model cannot predict whether a piece of content will go viral before it has been released; only its likely reach once it starts spreading. And the researchers think their approach to modelling should apply to information spreading via social networking sites and other online services as well as e-mail.

Moro's study agrees with his own results, says Liben-Nowell. "Many models of information propagation discount both the role of time and [differences between] people." But, there is more to discover, he says. For example, how people may vary in infectiousness depending on the type of content they receive.

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